Conversational Movement Dynamics and Nonverbal Indicators of Second Language Development: A Microgenetic Approach

This dissertation study extends on current understandings of gesture and embodied interaction with the eco-social environment in second language development (SLD) while introducing new aspects of movement analysis through dynamical modeling. To understand the role of embodiment during learning activities, a second language learning task has been selected. Dyads consisting of a non-native English-speaking student and a native English-speaking tutor were video recorded during writing consultations centered on class assignments provided by the student. Cross-recurrence quantification analysis was used to measure interactional movement synchrony between the members of each dyad. Results indicate that students with varied English proficiency levels synchronize movements with their tutors over brief, frequent periods of time. Synchronous movement pattern complexity is highly variable across and within the dyads. Additionally, co-speech gesture and gesture independent of speech were analyzed qualitatively to identify the role of gesture as related to SLD events. A range of movement types were used during developmental events by the students and tutors to interact with their partner. The results indicated that language development occurs within a movement rich context through negotiated interaction which depends on a combination of synchronized and synergistic movements. Synchronized movements exhibited complex, dynamical behaviors including variability, self-organization, and emergent properties. Synergistic movement emergence revealed how the dualistic presence of the self/other in each dyad creates a functioning intersubjective space. Overall, the dyads demonstrated that movement is a salient factor in the writing consultation activity

Investigation of chaos in biological systems

Chaos is the seemingly irregular behavior arising from a deterministic system. Chaos is observed in many real-world systems. Edward Lorenz’s seminal discovery of chaotic behavior in a weather model has prompted researchers to develop tools that distinguish chaos from non-chaotic behavior. In the first chapter of this thesis, I survey the tools for detecting chaos namely, Poincaré maps, Lyapunov exponents, surrogate data analysis, recurrence plots and correlation integral plots. In chapter two, I investigate blood pressure fluctuations for chaotic signatures. Though my analysis reveals interesting evidence in support of chaos, the utility such an analysis lies in a different direction that I point to the reader. In chapter three, I investigate a simulation of predator-prey interactions. My analysis casts doubt on some of the claims laid by past researchers, and I prompt future researchers to probe some specific questions that I have outlined in this thesis

Complexity Science in Human Change

This reprint encompasses fourteen contributions that offer avenues towards a better understanding of complex systems in human behavior. The phenomena studied here are generally pattern formation processes that originate in social interaction and psychotherapy. Several accounts are also given of the coordination in body movements and in physiological, neuronal and linguistic processes. A common denominator of such pattern formation is that complexity and entropy of the respective systems become reduced spontaneously, which is the hallmark of self-organization. The various methodological approaches of how to model such processes are presented in some detail. Results from the various methods are systematically compared and discussed. Among these approaches are algorithms for the quantification of synchrony by cross-correlational statistics, surrogate control procedures, recurrence mapping and network models.This volume offers an informative and sophisticated resource for scholars of human change, and as well for students at advanced levels, from graduate to post-doctoral. The reprint is multidisciplinary in nature, binding together the fields of medicine, psychology, physics, and neuroscience

Dimensionality reduction and unsupervised learning techniques applied to clinical psychiatric and neuroimaging phenotypes

Unsupervised learning and other multivariate analysis techniques are increasingly recognized in neuropsychiatric research. Here, finite mixture models and random forests were applied to clinical observations of patients with major depression to detect and validate treatment response subgroups. Further, independent component analysis and agglomerative hierarchical clustering were combined to build a brain parcellation solely on structural covariance information of magnetic resonance brain images. Übersetzte Kurzfassung: Unüberwachtes Lernen und andere multivariate Analyseverfahren werden zunehmend auf neuropsychiatrische Fragestellungen angewendet. Finite mixture Modelle wurden auf klinische Skalen von Patienten mit schwerer Depression appliziert, um Therapieantwortklassen zu bilden und mit Random Forests zu validieren. Unabhängigkeitsanalysen und agglomeratives hierarchisches Clustering wurden kombiniert, um die strukturelle Kovarianz von Magnetresonanz­tomographie-Bildern für eine Hirnparzellierung zu nutzen

Living and learning together : integrating developmental systems theory, radical embodied cognitive science, and relational thinking in the study of social learning

Behavioural scientists argue that ‘social learning’ provides the link between biological phenomena and cultural phenomena because of its role in the ‘cultural transmission’ of knowledge among individuals within and across generations. However, leading authors within the social sciences have proposed alternative ways of thinking about social life not founded on the Modern oppositions including nature-culture, biology-culture, body-mind, and individual-society. Similarly, the distinction between a domain of nature and a domain of nurture has also been extensively criticized within biology. Finally, advocates of ‘radical embodied cognitive science’ offer an alternative to the representational-computational view of the mind which supports the conventional notion of culture and cultural information. This thesis attempts to integrate developmental systems theory, radical embodied cognitive science, and relational thinking, with the goal to bring the field of social learning closer to these critical theoretical developments. In Chapter 2, I find no justification for the claim that the genome carries information in the sense of specification of biological form. Chapter 3 presents a view of ontogeny as a historical, relational, constructive and contingent process. Chapter 4 uses the notions of environmental information, abilities, affordances, and intentions to make sense of behaviour and learning. In Chapter 5, I argue that the notion of social learning can be understood in terms of relational histories of development rather than in terms of transmission of information. I then report empirical studies investigating behavioural coordination and social learning consistent with this theoretical framework. Chapter 6 presents evidence that dyads in a joint making activity synchronize their attention constrained by their changing situation and that coordination of attention is predictive of implicit and explicit learning. Chapter 7 presents evidence that joint attention does not require gaze following and that attentional coordination is predictive of learning a manual task. Together, these theoretical and empirical studies suggest a new way of thinking about how humans and other animals live and learn socially, one that is consistent with critical theoretical and philosophical developments that are currently neglected in the literature on social learning

Dopaminergic Modulation Shapes Sensorimotor Processing in the Drosophila Mushroom Body

To survive in a complex and dynamic environment, animals must adapt their behavior based on their current needs and prior experiences. This flexibility is often mediated by neuromodulation within neural circuits that link sensory representations to alternative behavioral responses depending on contextual cues and learned associations. In Drosophila, the mushroom body is a prominent neural structure essential for olfactory learning. Dopaminergic neurons convey salient information about reward and punishment to the mushroom body in order to adjust synaptic connectivity between Kenyon cells, the neurons representing olfactory stimuli, and the mushroom body output neurons that ultimately influence behavior. However, we still lack a mechanistic understanding of how the dopaminergic neurons represent the moment-tomoment experience of a fly and drive changes in this sensory-to-motor transformation. Furthermore, very little is known about how the output neuron pathways lead to the execution of appropriate odor-related behaviors. We took advantage of the mushroom body’s modular circuit organization to investigate how the dopaminergic neuron population encodes different contextual cues. In vivo functional imaging of the dopaminergic neurons reveals that they represent both external reinforcement stimuli, like sugar rewards or punitive electric shock, as well as the fly’s motor state, through coordinated and partially antagonistic activity patterns across the population. This multiplexing of motor and reward signals by the dopaminergic neurons parallels the dual roles of dopaminergic inputs to the vertebrate basal ganglia, thus demonstrating a conserved link between these distantly related neural circuits. We proceed to demonstrate that this dopaminergic signal in the mushroom body modifies neurotransmission with synaptic specificity and temporal precision to coordinately regulate the propagation of sensory signals through the output neurons. To explore how these output pathways ultimately influence olfactory navigation we have developed a closed loop olfactory paradigm in which we can monitor and manipulate the mushroom body output neurons as a fly navigates in a virtual olfactory environment. We have begun to probe the mushroom body circuitry in the context of olfactory navigation. These preliminary investigations have led to the identification of putative pathways for linking mushroom body output with the circuits that implement odor-tracking behavior and the characterization of the complex sensorimotor representations in the dopaminergic network. Our work reveals that the Drosophila dopaminergic system modulates mushroom body output at both acute and enduring timescales to guide immediate behaviors and learned responses

2016 IMSAloquium, Student Investigation Showcase

Welcome to the twenty-eighth year of the Student Inquiry and Research Program (SIR)! This is a program that is as old as IMSA. The SIR program represents our unending dedication to enabling our students to learn what it is to be an innovator and to make contributions to what is known on Earth.https://digitalcommons.imsa.edu/archives_sir/1026/thumbnail.jp

Enhancing Free-text Interactions in a Communication Skills Learning Environment

Learning environments frequently use gamification to enhance user interactions.Virtual characters with whom players engage in simulated conversations often employ prescripted dialogues; however, free user inputs enable deeper immersion and higher-order cognition. In our learning environment, experts developed a scripted scenario as a sequence of potential actions, and we explore possibilities for enhancing interactions by enabling users to type free inputs that are matched to the pre-scripted statements using Natural Language Processing techniques. In this paper, we introduce a clustering mechanism that provides recommendations for fine-tuning the pre-scripted answers in order to better match user inputs

Proceedings, MSVSCC 2018

Proceedings of the 12th Annual Modeling, Simulation & Visualization Student Capstone Conference held on April 19, 2018 at VMASC in Suffolk, Virginia. 155 pp